eigpca

PCA via eigen-decomposition of the covariance/correlation matrix.

Install

pip install eigpca

from eigpca import PCA
from sklearn.datasets import load_iris

X = load_iris().data
pca = PCA()

pca.fit(X)
pca.transform(X, n_components=2)

pca.plot(y="pov")

Scree plot

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution for eigpca 0.1.0
File	Size	Uploaded
eigpca-0.1.0.tar.gz	3.8 kB	Apr 11, 2020	Details

Table of built distributions (wheels) for eigpca 0.1.0
File	Interpreter	ABI	Platform	Reset
eigpca-0.1.0-py3-none-any.whl 3.9 kB Apr 11, 2020	Python 3	none	any	Details

Total release size: 7.7 kB

Download URL	eigpca-0.1.0.tar.gz
Size	3.8 kB
Tags	Source
SHA-256 checksum How to use checksums	`8e821b44fbe8a897cadd4ac64a1bd01b43121e13a26f0b5cfac503572cfa90e4`
BLAKE2b-256 checksum How to use checksums	`655df5963d6a9f9dfc637eb69c7430e6005c1883014b61e7eae219466b256c01`
Upload date	Apr 11, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.0.5 CPython/2.7.10 Darwin/18.0.0`

Download URL	eigpca-0.1.0-py3-none-any.whl
Size	3.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`84c365da6d2b58425ee5276e3dd52a4f83d97c0b2706484beb7644c3dabcb804`
BLAKE2b-256 checksum How to use checksums	`818eeb862c0c1d47ff15eb0130bdef771f15abe44c9da29212cd9c5566a5010d`
Upload date	Apr 11, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.0.5 CPython/2.7.10 Darwin/18.0.0`

This release

0.1.0 This release